Alua Salykbayeva

Ms. Alua Salykbayeva

Economics, University of Essex, Colchester, Essex, UK
+1
orcid

Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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23 October 2023 in Symmetry
Verified
Journal Article
Open access
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Seyit KerimkhulleSeyit Kerimkhulle
Seyit Kerimkhulle
ZDZhulduz Dildebayeva
Zhulduz Dildebayeva
ТТТохметов Акылбек Тимербекович
Тохметов Акылбек Тимербекович
AAAkzhibek Amirova
Akzhibek Amirova
JTJamalbek Tussupov
Jamalbek Tussupov
ASAlua Salykbayeva
Alua Salykbayeva
This article addresses the issue of information security in the Industrial Internet of Things (IIoT) environment. Information security risk assessment in the IIoT is complicated by several factors: the complexity and heterogeneity of the system, the dynamic nature of the system, the distributed network infrastructure, the lack of standards and guidelines, and the increased consequences of security breaches. Given these factors, information security risk assessment in the IIoT requires a comprehensive approach adapted to the peculiarities and requirements of a particular system and industry. It is necessary to use specialized risk assessment methods and to take into account the context and peculiarities of the system. The method of information security risk assessment in the IIoT, based on the mathematical apparatus of fuzzy set theory, is proposed. This paper analyzes information security threats for IIoT systems, from which the most significant criteria are selected. The rules, based on which decisions are made, are formulated in the form of logical formulas containing input parameters. Three fuzzy inference systems are used: one to estimate the probability of threat realization, another to estimate the probable damage, and a final one to estimate the information security risk for the IIoT system. Based on the proposed method, examples of calculating the information security risk assessment in the IIoT environment are provided. The proposed scientific approach can serve as a foundation for creating expert decision support systems for designing IIoT systems.